The <scp>r</scp> toolbox <scp>grainscape</scp> for modelling and visualizing landscape connectivity using spatially explicit networks
Bibliographic record
Abstract
Abstract Networks are widely used for modelling landscape connectivity and have many ecological and conservation applications. The nodes in these models describe geographic locations (such as habitat patches or protected areas) and links describe the potential for organisms (or their propagules) to move among nodes. We present the r package grainscape which facilitates working with these networks within a spatially explicit framework. Package analyses are based on the minimum planar graph, a class of network where links among nodes are influenced by the spatial characteristics of features across the entire landscape. Modelling outputs are compatible with downstream packages including igraph for network analysis and ggplot2 for visualization. Tools for analysis (e.g. finding corridors) and scaling networks (e.g. grains of connectivity) are also provided. Models can be exported for visualization and analysis in Geographic Information System (GIS) or network software. This package provides a computationally‐efficient programmatic toolbox for many landscape connectivity research questions, enabling researchers to easily customize models, work at large geographic extents, generate their own network metrics, conduct sensitivity analyses and seamlessly employ r statistical functions to test models using biological data. A detailed guide, provided as an Appendix, illustrates common analysis and model variants with accompanying r code.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".